Field performance evaluation of asphalt mixtures containing high percentage of RAP using LTPP data

Construction and Building Materials 176 (2018) 118–128

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Field performance evaluation of asphalt mixtures containing high percentage of RAP using LTPP data Hongren Gong a,1, Baoshan Huang b,a,⇑,2, Xiang Shu a,3 a b

The University of Tennessee, Knoxville, TN 37996, United States Tongji University, China

h i g h l i g h t s
Graphical data representation was provided to identify critical factors to asphalt overlays performance.
RAP overlays had more alligator cracking than virgin asphalt.
Less cracking but more rutting was found in thick overlays.
Mill & Fill was beneficial for all performance measures, especially for the alligator cracking, transverse cracking and roughness.
RAP overlays with fine subgrade soil and from a cold region had more rutting than the corresponding virgin asphalt overlays.

a r t i c l e

i n f o

Article history: Received 13 January 2018 Received in revised form 30 April 2018 Accepted 1 May 2018

Keywords: Recycled asphalt pavements Field performance Logistic regression High percentage LTPP

a b s t r a c t Recycled asphalt pavement (RAP) has been used in production of new mixtures for many decades and has become a common practice in highway agencies. However, concerns about field performance of RAP mixtures have been lingering on since the beginning of RAP usage. In this study, the field performance of RAP was verified through the Long-Term Pavement Performance (LTPP) program. Particularly, pavement overlays constructed with virgin asphalt and those constructed with a significant RAP percentage were examined and compared regarding their field performance. To achieve this goal, data from the Specific Pavement Study experiment 5 (SPS-5) of LTPP were retrieved and statistically analyzed for comparison. A total of eighteen projects comprised of 162 sections were collected. Various performance measures were incorporated, including alligator cracking, longitudinal cracking (wheel path and non-wheel path), transverse cracking, patching, potholes, rutting, roughness, and pavement surface deflections. In addition to the key variables used in the SPS-5 experiment (thickness, materials and pre-overlay treatment), the impacts of other important factors, including climatic conditions, initial surface condition and subgrade soil classification, were also investigated. Exploratory data analyses and logistic regression were used to compare the performance of RAP and virgin asphalt overlays. Results from the analysis results show that RAP had little effects on longitudinal cracking, transverse cracking, and roughness, but slightly increased the risk of fatigue cracking and weakened pavement structure. As expected, use of RAP was beneficial in reducing the rutting potential of thick overlays. For both RAP and virgin asphalt overlays, thicker overlays performed better than thinner ones in all performance measures except for rutting. Pre-overlay treatment methods and subgrade soil types were found to be critical factors affecting fatigue cracking and roughness. The initial surface condition also showed significant effects on pavement structural capacity and fatigue cracking over the long run. Ó 2018 Published by Elsevier Ltd.

1. Introduction ⇑ Corresponding author. E-mail addresses: [email protected] (H. Gong), [email protected] (B. Huang), [email protected] (X. Shu). 1 Graduate Research Assistant. 2 Ph.D., P.E., Edwin Burdette Professor. 3 Ph.D., Research Assistant Professor. https://doi.org/10.1016/j.conbuildmat.2018.05.007 0950-0618/Ó 2018 Published by Elsevier Ltd.

Use of reclaimed asphalt pavement (RAP) into asphalt mixtures has been gaining ever-increasing popularity in the paving industry because of its economic and environmental benefits. Extensive laboratory research work has been conducted on evaluating the effects of including RAP into asphalt mixtures, such as the properties of recycled asphalt binder with RAP [1,2] and asphalt

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mixture containing RAP [3–5]. Kandhal and Foo [3] suggested that up to 15% of RAP can be used without adjusting the asphalt binder’s performance grade. However, when more than 25% percentage of RAP is used, the RAP significantly affects the physical properties of asphalt binder [6]. Other laboratory studies have observed better fatigue performance in asphalt mixtures containing up to 30% RAP [4], while poorer fatigue resistance is found in asphalt mixtures containing 50% RAP [5]. Despite numerous laboratory studies have been conducted on including a significant amount of RAP into the production of asphalt mixtures, however, few has done to verify the findings in laboratory with the field observed performance. This study aims to investigate the long-term in-service performance of asphalt mixture overlays containing a significant amount (30%) of RAP and compare them with virgin asphalt mixtures overlays. Performance data used for this purpose are from the Specific Pavement Study experiment 5 (SPS-5) of the long-term pavement performance (LTPP) program. The SPS-5 experiment was designed to study the effects of rehabilitation strategies for flexible pavements [7]. A total of eighteen projects in the Unites States (U.S.) and Canada were constructed, and each project has eight test sections with varied overlay thickness, pre-overlay treatment methods, materials types, and a control section that with no overlay and treatment for reference. These sections are also called the core 9 section [7]. In addition to the core 9 sections, Alabama, Arizona, Florida, Georgia, and New Jersey included some supplemental sections concerning the performance of RAP overlays in the SPS-5 experiment. However, very few performance observations were available in these sections, they were thus excluded from this study. It is noted that several studies have been carried out using the SPS-5 data [8–12]. At a relatively early stage, Hall et al. [8] did a side-side comparison for different overlay strategies applied in the SPS-5, they reported that no significant difference is found between overlays using virgin asphalt mixtures and asphalt mixtures containing RAP in terms of cracking, roughness and rutting. Carvalho et al. [9] presented an exploratory study on the shortand long-term performance of asphalt mixtures overlays containing RAP. They observed that although RAP mixes can achieve equivalent performance to virgin asphalt mixtures overlays in most of the cases, the RAP mixes used in thicker overlays are more likely on par with virgin asphalt mixtures overlays. West et al. [10] used seven types distress as performance indicators to compare the performance of RAP overlays with virgin asphalt mixtures overlays. Their results showed that thicker overlays outperform the thinner ones for all distresses except rutting, and milling before rehabilitation tends to increase the rutting risk. Another study discussed the effects of different overlay strategies on the initiation of three types cracking, but their interests were not focused on the materials [11]. Wang [12] used a parametric survival analysis to examine the service life of RAP overlays, and he reported that given the same overlay thickness and preoverlay treatment, virgin asphalt mixtures overlays have higher survival probabilities than RAP overlays in terms of fatigue cracking. Although these studies have generated valuable information for understanding the performance of RAP overlays, most were conducted in the early lives of the test sections. There is a need to exploit performance data with longer monitoring history and include more factors to depict a comprehensive and accurate picture for the long-term performance of RAP overlays. This study attempts to include more distress types as performance measures and the deflection as structural parameters to gain insight into the effects of including RAP in asphalt mixtures overlays.

2. Objective and scope The objective of this study was to investigate the long-term inservice performance of pavement overlaid with virgin asphalt mixtures and those containing a significant amount of RAP. In addition to the overlay material type, various other factors were also examined, including the overlay thickness, pre-overlay preparation methods, climatic condition, subgrade soil classification and initial surface condition. An exploratory data analysis was conducted for the factors considered, and logistic regression models were developed to identify the critical factors and quantify their impacts on the performance indicators. 3. Specific pavement study-5 experiment The long-term pavement performance (LTPP) program is managed by the FHWA and has monitored in-service pavement performance around 30 years. LTPP included a series of experiments to investigate the optimal strategies for pavement maintenance and rehabilitaion, such as the Specific Pavement Study-5 (SPS-5) experiment for asphalt mixtures overlays strategies. In the SPS-5 experiment, each test section consists of two lanes, which is 152 m (500 ft) long and 7.32 m wide (24 ft). The key factors involved in this experiment are the mix types (virgin asphalt mixtures or asphalt mixtures containing RAP), pre-overlay treatment method, and overlay thickness (Table 1). As shown in Table 1, sections 0502 through 0505 have minimal pre-overlay surface preparation, which indicates that only patching was performed before overlay; sections 0506 through 0509 have intensive surface preparation, which implies that around 51 mm of the surface was milled off and patching was performed to correct localized failures [7]. There are several other factors involved in this experiment, including initial surface condition, subgrade soil classification, climatic conditions (precipitation and temperature). Table 2 presents a detailed factorial for these factors. In this table, the fair surface condition was defined as a pavement with fatigue cracking and rutting less than 10%, and 0.25 in (6.4 mm), respectively. Otherwise, the surface condition was treated as poor. The climatic regions in Table 2 are delineated by precipitation and freeze index. Sites with an average annual rainfall greater than 1,000 mm are classified as wet, otherwise as dry. Similarly, a site with a freeze index greater than 60° C/days are considered as freeze, otherwise as no-freeze. The freeze index is defined as the negative of the sum of all average daily temperature below 0° C in a year. A higher freeze index indicates a severer freeze condition. The locations of the SPS-5 project sites are shown in Fig. 1. As presented in Fig. 1, the Manitoba site of Canada has the longest performance monitoring history after rehabilitaion (23.3 years), followed by the Alabama (22.1 years) and Florida (16.7 years) sites. There are a total of thirteen sites with a performance monitoring Table 1 SPS-5 Experiment Key Factors. Section ID

Mix Type

Overlay Thickness (mm)

Pre-Overlay Preparation

0501 (Control) 0502 0503 0504 0505 0506 0507 0508 0509

No Overlay RAP RAP Virgin Virgin Virgin Virgin RAP RAP

None 51 127 127 51 51 127 127 51

None Minimal Minimal Minimal Minimal Intensive Intensive Intensive Intensive

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Table 2 Projects and Factorial of SPS-5 Experiment Design. Pre-Overlay Surface Condition

Soil Type

Climatic Region Wet-Freeze

Wet-No-Freeze

Dry-Freeze

Dry-No-Freeze

Fair

Coarse Fine

GA, NJ —

— —

CO, AB, MT MN

NM OK, TX

Poor

Coarse Fine

ME MD, MO

FL, AL MS

MB —

AZ, CA —

Fig. 1. Locations and service age of SPS-5 project sites (Abbreviations: GA, Georgia; CO, Colorado; NJ, New Jersey; AB, Alberta (Canada); MT, Montana; NM, New Mexico; MN, Minnesota; OK, Oklahoma; TX, Texas; MB, Manitoba (Canada); CA, California; ME, Maine; FL, Florida; AL, Alabama; AZ, Arizona; MD, Maryland; MO, Missouri; MS, Mississippi).

period longer than ten years and five sites have performance monitoring period less than ten years. Generally, the design life of overlay is ten years. Hence, in this study, performances monitored longer than five years were considered as long-term performance. Hence, the site with less than five years of performance monitoring after rehabilitaion was excluded from the analyses.

4. Data preparation In this study, three categories of data were incorporated: distresses, riding quality and structural information. The distress indicators considered were fatigue cracking, longitudinal cracking in both wheel path and non-wheel path areas, transverse cracking, potholes, and patches. Two data types were used to represent the riding quality, the rutting and roughness in terms of international roughness index (IRI). The pavement structural capacity was characterized by the maximum deflection measured by a falling weight deflectometer (FWD), and the area under pavement profile (AUPP).

The following provides a brief description for these performance indicators.
Distresses. Fatigue cracks are interconnected cracks that initiates at the bottom of asphalt layer, and is caused by repeated traffic loading. Wheel path (WP) longitudinal cracking are usually a type of surface-down fatigue crack that happens at the edge of a wheel path. Non-wheel path (NWP) longitudinal cracks are cracks parallel to the pavement centerline occurred in the NWP area, which is frequently attributed to poor joint construction. Transverse cracking is a type of cracking perpendicular to the pavement centerline, which is also called thermal cracking and usually caused by rapid temperature Decreases. Potholes are bowel-shaped holes in the pavement surface. Patches are areas of pavement that have been removed and replaced to correct localized defects [13].
Riding quality data. Rutting is a longitudinal depression in the wheel path, which is the result of consolidation of or plastic flow of asphalt mixture under wheel loads. According to a

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review by Fwa et al. [14], a rut depth greater than 5.1-mm (0.2in) may cause hydroplaning risk. Roughness is the longitudinal irregularity along pavement surface, which considers most pavement defects affecting the riding quality.
Structural information. Deflection is an indicator of overall pavement strength, and studies have observed that it has strong correlation with the structure number and pavement health condition [15,16]. In the this study, two indicators were used: maximum deflection and area under the pavement profile (AUPP). The AUPP is included because several studies have show it is tied to the tensile strain at the bottom of asphalt layer [17,18]. AUPP is defined as follows (Eq. 1):

AUPP ¼

1 ð5d0  2d1  2d2  d3 Þ 2

ð1Þ

where d0 is the deflection measured at the center of the loading plate of FWD, d1 ; d2 , and d3 are the deflections measured at 12inch, 24-inch, and 36-inch from the center of loading plate. In the LTPP database, the cracking data are recorded in three severities (high, medium and low), which were added up in this study to represent the overall situation. For the FWD deflection data, due to the loading and temperature dependence of asphalt mixtures, the deflections are temperature and loading condition dependent as well. To compare deflections measured at different loads and temperatures, the deflections were first normalized to the same load of 9,000-lb and then corrected to a reference temperature of 20° C (67 °F) according to the procedure proposed in literature [19]. In this study, all the data are retrieved from the latest version of the web-based LTPP database: www.infopave.com.

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particular category. Eq. (2) shows the typical form of logistic regression for a binary response variable.

PrðC ¼ 1jX ¼ xÞ ¼ PrðC ¼ 0jX ¼ xÞ ¼

expðb0 þ bT xÞ 1 þ expðb0 þ bT xÞ 1

ð2Þ

1 þ expðb0 þ bT xÞ

where PrðC ¼ 1jX ¼ xÞ is the probability of the response belongs to category 1 ðC ¼ 1Þ given the factors included (X), b’s are the model estimates, and PrðC ¼ 0jX ¼ xÞ is the probability of the response belongs to category 0 (C ¼ 0) given the factors X. A logit transformation can turn this model into a linear form as shown in Eq. (3), and the left side of the equation is also called log-odds. The odds is the ratio of probability of success to the probability of failure. Given an event, if the odds are greater than 1, then it is more likely to succeed than fail.

log

PrðC ¼ 1jX ¼ xÞ ¼ b0 þ bT x PrðC ¼ 0jX ¼ xÞ

ð3Þ

6. Long-term performance results and comparison In this section, exploratory data analyses were conducted first, which comprised of representing the data in boxplots and paired t-tests for each pair of RAP overlays and virgin asphalt mixtures overlays. It was followed by developing logistic regression models to identify the critical factors and quantify their the impacts on the performance of RAP and virgin asphalt mixtures overlays. 6.1. Exploratory data analysis

5. Methodology Two types of methods were used in this study: exploratory data analysis (EDA) and logistic regression. The purpose of EDA is to better understand the data, such as obtaining central tendency and variation of the data, and even correlations between each pair of variables [20]. The EDA in this study consists of two parts, graphical representation of data and side-by-side comparison through paired t-tests. Graphical representation of the data is frequently used in EDA. Among the many popular graphical methods, the box-and-whiskers plot (boxplot) is a powerful yet succinct tool to characterize the central tendency and variation of the data [20]. The boxplot is constructed by drawing a box between the first and third quartiles with a solid line drawn across the box to locate the median, and sometimes a mark is also added to indicate the group mean. Given the many merits of boxplot, it was used as the major graphical tool for EDA in this study. In addition, paired t-tests were conducted for each pair of sections. The paired t-tests suit the experiment design in the study well, because all sections were designed in pair in the SPS-5 experiment, namely, Sections 0502 versus 0505, 0503 versus 0504, 0509 versus 0506, and 0508 versus 0507. The problem of interest in this study is to investigate under what conditions the performance differences between virgin asphalt mixtures overlays and RAP overlays are significant, which is a binary problem. It is also noted that all the factors involved in this study are either ordinal or binary variables. The logistic regression is one of the most popular methods for dealing with qualitative data, and it has been utilized to investigate the effectiveness of preventive maintenance and cracking initiation [21,22]. Using the LTPP SPS-3 data, Wang [23] developed ordinal logistic regression models to predict the probability of multiple levels of severity for alligator cracking. Unlike linear regression, which predicts the quantity of the response variable, the logistic regression predicts the probability of the response belongs to a

Fig. 2 offers boxplots for the distress indicators, and Table 3 shows the paired t-test results for these indicators. In Table 3, the numbers in bold indicate there is a significant performance difference between the pair of sections. It was observed from Fig. 2a and Table 3, overall, the SPS-5 sections work well in terms of fatigue cracking, with a average less than 50-m2 and a maximum of 285-m2. For both pairs of sections with thin overlays, more fatigue cracks were observed in the ones containing RAP. For thick overlays without milling, the RAP overlays showed more fatigue cracking. However, for thick overlays with intensive pre-overlay treatment, the inclusion of RAP did not increase the risk of more fatigue cracking. Regarding the wheel path longitudinal cracking (Fig. 2b), the overall level of this type of distress in the SPS-5 sections is low, with a mean of 6.2-m and a maximum of 300.8-m. As Fig. 2b and the results in Table 3 implicated, more longitudinal cracking was found in thick RAP overlays with minimal pre-overlay treatment and thin RAP overlays with intensive preoverlay treatment. As Fig. 2c displayed, for non-wheel path longitudinal cracking, the use of RAP is not a critical factor. It is seen in Fig. 2d, more transverse cracks were found in both thick RAP overlays (Sections 0503 and 0508). For potholes, as there was very few occurrence of this distress in the SPS-5 sections, the total number of potholes in all SPS-5 sections was 64, and most of the potholes were found in the control section of the Alberta project. Similar to the potholes, quite a few patches were found in the SPS-5 sections, the mean patch area is around 4-m2, and most of the patches were found in the Maryland project site. In the Maryland site, more patches were found in the thin RAP overlays with minimal pre-overlay treatment than the virgin asphalt mixtures counterpart. Figs. 4 and 3 offer two boxplots for the rutting and IRI, and the paired t-test results were shown in Table 4. As is seen in Fig. 3, the IRI ranged from 0.43 to 5.26-m/km, with an average of 1.23-m/km. For both pairs of thick overlays, there is no sharp contrast in IRI for

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Fig. 2. Effects of overlay strategies on pavement distresses.

Table 3 Paired t-test results for distress measures. RAP-Virgin

Fatigue (m2)

LONG-WP(m)

LONG-NWP(m)

Transverse(m)

Potholes(number)

Patches(m2)

0502–0505

DOM 95% CI

78.34–41.65 (14.55, 58.83)

8.60–10.35 (8.99, 5.50)

94.99–95.45 (23.62, 22.71)

35.72–41.69 (16.41, 4.46)

0.007–0.019 (0.037, 0.013)

0.93–6.84 (12.21, 0.40)

0503–0504

DOM 95% CI

29.47, 13.4 (5.26, 26.89)

11.67–3.60 (1.64, 14.49)

106.0–102.2 (18.91, 26.49)

31.49–20.08 (2.53, 20.29)

0.014–0.00 (0.013, 0.04)

1.47–9.93 (14.8, 2.12)

0509–0506

DOM 95% CI

50.63–20.64 (13.95, 46.03)

6.97–3.04 (0.11, 7.75)

86.48–82.16 (16.98, 25.63)

28.55–26.36 (7.71, 12.10)

0.076–0.006 (0.022, 0.12)

1.59–0.43 (0.28, 2.59)

0508–0507

DOM 95% CI

18.52–13.79 (3.20, 23.66)

6.98–4.73 (2.43, 6.94)

86.48–82.16 (16.98, 25.63)

27.68–18.22 (0.20, 18.72)

0.00–0.013 (0.03, 0.005)

0.298–0.35 (0.43, 0.32)

NOTE:

1) DOM = difference of mean; CI = confidence interval; LONG = longitudinal cracking; WP = wheel path NWP = non-wheel path; 2) Parenthesized numbers in bold indicate significant differences.

RAP and virgin asphalt mixtures overlays (Fig. 3). Regarding the thin overlays with minimal and intensive pre-overlay treatment (sections 0509 and 0506), the IRI of RAP overlays were slightly higher than the corresponding virgin asphalt mixtures overlays. As for the rutting, a slightly higher rutting value was found in thin RAP overlays with minimal (section 0502) and intensive preoverlay treatments (section 0509) than their corresponding virgin asphalt mixtures overlays (Fig. 4). There is no stark difference in rutting for the pair of sections with thick overlays and minimal pre-overlay treatments, while less rutting was found in the thick RAP overlays received intensive pre-overlay treatment than the corresponding virgin asphalt mixtures overlays. The t-tests results for the deflection parameters were included in Table 4 as well. As expected, the average maximum deflection in the thick overlays are lower than the thin overlays (Fig. 5a). For overlays received only minimal pre-overlay treatment, larger maximum deflections (d0 ) were found in the RAP overlays.

Whereas for sections received intensive pre-overlay treatments, the thin overlays with RAP showed larger d0 than the virgin asphalt mixtures overlays, and no difference was found in the thick overlays of this category. Similar observations were found in the boxplot for the AUPP (Fig. 5b). 6.2. Logitistic regression Logistic regression (LR) requires the response variable to be categorical. As such, the modeling process of LR was started with creating a categorical response. This was implemented in two steps. The first step is to obtain the performance differences between a section and the reference section. The followed step is to create a label variable and assign it the value of 1 if the difference is positive, otherwise assign it 0. Naturally, the control sections in the SPS-5 experiment are optimal reference sections for this purpose. However, there are three states have no control section, including

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Fig. 3. Influence of overlay strategies on IRI.

Fig. 4. Influence of overlay strategies on rutting. Fig. 5. Influence of overlay strategies on structural capacity.

California, Florida and Texas, and they all have a relatively long performance monitoring history (Fig. 1). It would be a huge miss if they were excluded from data pool for modeling due to the lack of control section. Therefore, instead of using the SPS-5’s control section (0501) as the reference section, the section overlaid with thin virgin asphalt mixtures and received only minimal preoverlay treatment (0505) was used as the reference. This decision is somewhat arbitrary, in effect, any section can serve as the reference section. Because the main purpose of this study is to compare the performances of RAP and virgin asphalt mixtures overlays, the data from control sections were excluded at this modeling stage. In

addition to the response variable, seven binary predictors were created as well: initial surface condition (fair vs. poor), subgrade soil classification (coarse vs. fine), precipitation (dry vs. wet), temperature (freeze or no-freeze), materials (RAP vs. virgin), thickness (thick vs. thin), and pre-overlay treatment (intensive vs. minimal). Table 5 presents the model results for eight performance measures incorporated in the study, and Fig. 8 gives the boxplots for the most critical factors in each model. The models for potholes and patches were omitted due to insufficient data. Because all

Table 4 Paired t-tests results for IRI, rutting and structural measures. IRI (m/km)

Rutting (mm)

d0 ðlmÞ

AUPP (lm)

0502–0505

DOM 95% CI

1.418–1.227 (0.0837, 0.299)

5.01–4.34 (0.248, 1.097)

218.9–194.7 (0.68, 47.64)

90.31–74.54 (4.51, 54.45)

0503–0504

DOM 95% CI

1.133–1.153 (0.102, 0.062)

5.147–5.048 (0.378, 0.578)

133.2–113.6 (2.08, 36.99)

90.31–74.54 (3.29, 28.30)

0509–0506

DOM 95% CI

1.282–1.122 (0.049, 0.27)

4.94–4.51 (0.044, 0.896)

186.8–172.3 (8.9, 37.9)

139.6–126.7 (8.2, 34.11)

0508–0507

DOM 95% CI

1.022–1.078 (0.119, 0.007)

4.99–5.45 (0.957, 0.043)

119.0–119.2 (15.97, 15.55)

76.3–75.1 (8.9, 11.3)

RAP-Virgin

NOTE:

1) DOM = Difference of mean; CI = Confidence; interval. 2) Parenthesized numbers in bold indicate significant differences.

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the factors considered are of the same scale (binary), the absolute value of each estimate denotes its relative importance to the model, and the sign of the estimate implicates the direction of

impacts. Recalling that the LR model shown in Eq. (3), the LR model predicts the log odds. The odds in this case was the ratio of probability of a section performed better than the reference to

Table 5 Logistic regression models results. Model Estimates

Fatigue

WP Long.

NWP Long.

Transverse

Rutting

IRI

d0

AUPP

Intercept Surface Condition (poor) Subgrade (fine) Precipitation (wet) Temperature (no-freeze) Materials (RAP) Thickness (thin) Treatment (minimal)

0.013 0.759 0.998 0.156 0.343 0.411 1.056 0.883

0.077 0.005 1.107 0.824 0.771 0.041 0.296 0.306

0.047 0.443 0.364 0.585 0.612 0.050 0.179 0.530

1.132 0.670 0.052 0.476 0.511 0.065 0.778 0.635

1.107 0.213 0.725 0.638 0.533 0.240 0.149 0.258

1.050 0.016 0.282 0.205 0.098 0.056 0.673 0.647

1.675 1.014 0.138 0.065 0.214 0.165 1.926 0.451

1.728 1.388 0.388 0.204 0.052 0.141 1.902 0.753

Error Rate

0.028

0.025

0.034

0.032

0.018

0.014

0.028

0.027

Notes: WP = wheel path; NWP = non-wheel path; Long.=longitudinal cracking. Numbers in bold indicate important variables to the model.

Fig. 6. Boxplots for different types of cracking.

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Fig. 6 (continued)

the probability of performing worse. If the estimate is positive, then the odds that a section outperforms the reference section are greater than 1. To illustrate with the LR model for fatigue cracking, the estimate for material type (RAP) is 0.411, which means that controlling all other factors, the odds that a RAP overlay performed better than the corresponding virgin asphalt mixtures overlay is e0:411 ð0:63Þ. In other words, the RAP overlay was less likely to outperform the virgin asphalt mixtures overlay in fatigue cracking. The following observations were made through interpreting the models in Table 5.


For fatigue cracking, the most important factors were overlay thickness, subgrade type, pre-overlay treatment extent and initial surface condition. The model can be interpreted as controlling all other factors, thin overlays are 2.9 times more likely than thick overlays to develop more fatigue cracking than the reference section. Sections applied only minimal pretreatment were 2.4 times more likely to have more fatigue cracking than the reference sections. RAP overlays are only 0.63 times likely to outperform the virgin asphalt mixtures overlays in terms of fatigue cracking. As presented in Fig. 6a, most fatigue cracks

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Fig. 7. Boxplots for critical factors to IRI and rutting.

were found in thin overlays with coarse subgrade from the freeze area, and sections with fine subgrade and fair initial surface conditions showed the least fatigue cracking.
Regarding the wheel path (WP) longitudinal cracking, the use of RAP has no significant impact on the development of this performance indicator. Overall, the SPS-5 projects worked well in terms of WP longitudinal cracking. The most important factors for this distress were the subgrade soil classification, and climatic factors. Sections from the dry area and with fine subgrade showed the least WP longitudinal cracking (Fig. 6b).
For non-wheel path (NWP) longitudinal cracking, the use of RAP or not was not a critical factor. The most important factors for this indicator were the climatic factors and pre-overlay treatment method. As displayed in Fig. 6c, sections with thin overlays from the wet no-freeze area had the least NWP longitudinal cracking.


For transverse cracking, the use of RAP or not was not a critical factor. The most important factors for transverse cracking were overlay thickness, precipitation, pre-overlay treatment, and initial surface condition (Fig. 6d). The thick overlays helped to retard transverse cracking, while a dry weather aggravated this distress.
For pavement roughness (IRI), the overlay material was not a important factor. The overlay thickness and pre-overlay treatment had strong impacts on the IRI. Thick overlays with intensive pre-overlay treatment tended to have smoother surfaces. A fine subgrade helped to maintenance the smoothness of pavement surface, whereas more precipitation contributed to increase in roughness (Fig. 7a).
For rutting, although the use of RAP was not the most important factor, the practice of including RAP into overlays helped reduce rutting, especially for sections in the relative warm area. The

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Fig. 8. Boxplots for critical factors to pavement structural capacity.

subgrade soil classification and climatic factors affected the rutting most. For sections in the cold region, more rutting was observed in the RAP sections with fine subgrade soil. However, for RAP sections with fine subgrade soil from a warmer region, the opposite was found true (Fig. 7b).
For structural information, the most important factors affecting the maximum deflections were the overlay thickness, initial surface condition and pre-overlay treatment method. As expected, a thick overlay was beneficial to the pavement structure. However, the use of RAP was not detrimental to pavement structural capacity. Milling the existing surface before rehabili-

taion contributed to enhance the structural capacity of pavement. The climatic factors showed no strong impact on the pavement structure capacity (Fig. 8a). The model and the corresponding boxplot for AUPP confirmed these observations (Fig. 8b). 7. Conclusions In this study, efforts were made to employ the LTTP data to verify the field performance of pavements built with RAP. The longterm in service performance data of asphalt overlays containing a

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significant RAP content were retrieved from the LTPP SPS-5 experiment and statistically analzyed. Various pavement performance indicators including distresses, rut, roughness, and pavement structural information were included for comparison. Other important factors affecting pavement performance were also considered in the analysis, including overlay thickness, overlay material, preoverlay preparation, initial surface condition, subgrade soil classification and climatic conditions represented by temperature and freeze index. Performance differences between RAP and virgin asphalt mixtures overlays were compared using two methods: exploratory data analyses and logistic regression, to identify the critical factors and quantify their impacts. Major findings are shown as follows.
Overall, the use of RAP for rehabilitation reduced the potential for rutting, while it had little impacts on the wheel path and non-wheel path longitudinal cracking, transverse cracking and roughness. However, including RAP into virgin asphalt mixtures for rehabilitation slightly increased the risk of fatigue cracking, and probably may weaken the pavement structure in the long run.
For both RAP and virgin asphalt mixtures overlays, the thick (127-mm) overlays performed better than the thin (54-mm) overlays in terms of fatigue cracking, wheel path and nonwheel path longitudinal cracking, transverse cracking, IRI, and structural capacity with the exception of rutting.
Intensive pre-overlay preparation or removing the existing failed surface before applying the overlay was beneficial for all the performance measures, particularly for fatigue cracking, transverse cracking and roughness.
Climatic conditions (precipitation and temperature) were influential factors influences to the WP and NWP longitudinal cracking and transverse cracking, especially on overlays from sites with fine subgrade soil. The transverse cracking in the sections in the wet area was significantly fewer than those in dry area.
The initial surface condition showed significant effects on pavement structural capacity and fatigue cracking.
Subgrade soil classification also showed strong impacts on the fatigue cracking, wheel path longitudinal, rutting and IRI. In the wet and no-freeze area, sections with coarse subgrade soil experienced significantly lower rutting than those with fine subgrade soil. However, an opposite trend was found in the sections from sites in the dry freeze area, where sections with fine subgrade soil showed lower rutting. In the wet no freeze area, the IRI was significantly lower for sites with coarse subgrade soil than those with fine subgrade soil. Conflicts of interest The authors declared that there is no conflicts of interest.

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